Apprenticeships as pathway to care careers: Ethical challenges and opportunities for professions

Editorial

A study on emission reduction and combustion efficiency, analyzing oxymethylene ether (OME1-5) with diesel fuel

Data availability: Data will be made available on request.This study investigates an optimized fuel blend comprising oxymethylene ethers (OMEn = 1–5 series) with diesel aimed at simultaneously reducing soot and NOx emissions while enhancing fuel efficiency. An optimal blend was identified through rigorous experimentation and computational fluid dynamics (CFD) modeling. The study employs the response surface method (RSM) for regression analysis and integrates machine learning techniques for predictive modeling to assess various fuel compositions and optimize the mixture for improved combustion dynamics. Experimental measurements were conducted in an optical constant volume combustion chamber (CVCC) to confirm the blend’s effectiveness in reducing both soot and NOx emissions. The investigation thoroughly analyzes spray combustion properties, including injection duration, Start of Combustion (SOC), End of Combustion (EOC), Lift-Off length of fuels, spray tip penetration, and their impact on combustion efficiency. Analysis of energy densities between the blends reveals that OMED exhibits a heating value superior to OME2-5 but inferior to diesel, striking a balance in energy output. Furthermore, OMED demonstrates superior energy density compared to OME1-3 and diesel, highlighting its potential for enhanced fuel efficiency. The optimized blend achieves a significant 78.2 % reduction in soot emissions and a 31.3 % reduction in NOx emissions compared to conventional diesel, underscoring its efficacy in mitigating harmful emissions without compromising combustion performance. This research contributes valuable insights into developing sustainable fuel solutions for diesel engines, paving the way for greener automotive technologies in the future.This research was financially supported by the College of Engineering Design and Physical Sciences at Brunel University London under grant number 11667100

Dynamic Fashion Video Synthesis from Static Imagery

Data Availability Statement: This paper did not generate any new data.Online shopping for clothing has become increasingly popular among many people. However, this trend comes with its own set of challenges. For example, it can be difficult for customers to make informed purchase decisions without trying on the clothes to see how they move and flow. We address this issue by introducing a new image-to-video generator called FashionFlow to generate fashion videos to show how clothing products move and flow on a person. By utilising a latent diffusion model and various other components, we are able to synthesise a high-fidelity video conditioned by a fashion image. The components include the use of pseudo-3D convolution, VAE, CLIP, frame interpolator and attention to generate a smooth video efficiently while preserving vital characteristics from the conditioning image. The contribution of our work is the creation of a model that can synthesise videos from images. We show how we use a pre-trained VAE decoder to process the latent space and generate a video. We demonstrate the effectiveness of our local and global conditioners, which help preserve the maximum amount of detail from the conditioning image. Our model is unique because it produces spontaneous and believable motion using only one image, while other diffusion models are either text-to-video or image-to-video using pre-recorded pose sequences. Overall, our research demonstrates a successful synthesis of fashion videos featuring models posing from various angles, showcasing the movement of the garment. Our findings hold great promise for improving and enhancing the online fashion industry’s shopping experience.Engineering and Physical Sciences Research Council (EPSRC) grant number EP/T518116/1

A UNIFIED THEORY FOR ARMA MODELS WITH VARYING COEFFICIENTS: ONE SOLUTION FITS ALL

......Alessandra Canepa acknowledges financial support under the National Recovery and Resilience Plan (NRRP), Mission 4, Component 2, Investment 1.1, Call for tender No. 104 published on 2.2.2022 by the Italian Ministry of University and Research (MUR), funded by the European Union – NextGenerationEU– Project Title 20223725WE - Methodological and computational issues in large-scale time series models for economics and finance – CUP J53D23003960006- Grant Assignment Decree No 967 adopted on 30/06/2023 by the Italian Ministry of Ministry of University and Research (MUR)

Anticipation, recognition, evaluation, and control of indoor environmental hazards impacting Syrian refugees in Lebanon

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonA pilot study of indoor air quality in Syrian refugee settlements in Lebanon found indoor mould growth significantly linked with moisture and ventilation levels. A follow-up cross-sectional study was subsequently performed in 4 provinces of Lebanon. It was revealed that although non-residential shelters had the highest mean total indoor count (TIC), 3 mould genera were strongly associated with non-permanent shelters (p<.001) and occupancy was found strongly associated with some of the genera. Regarding shelter conditions, highest TIC was observed in unfinished structures. These findings suggest shelter category, condition and occupancy significantly influence indoor mould concentrations and may lead to increased respiratory health risks for Syrian refugees in Lebanon. Biomonitoring using the fractional exhaled breath nitric oxide (FENO) biomarker and clinical interpretation of results suggested potential persistent exposure to allergens. Two mitigation technologies were developed for deployment in non-permanent shelters: Solar-powered Window Air Cleaning (SWAC) and Solar-powered Wall Air Vent (SWAV). Operating at 100% outdoor air intake, the SWAC unit exceeded the ASHRAE standard 62.2 minimum requirement for an average refugee household occupancy (n=6) and total floor area (56 m2) and met equivalent outdoor air requirements for the most stringent ASHRAE standard 52.2 particle range (0.3 – 1.0 μm) operating at 50% outdoor air. The SWAV unit exceeded ASHRAE ventilation requirements for individual refugee rooms (15 m2) at average occupancy. In conclusion, this project provides a rare insight into the poor indoor air quality of refugee shelters in Lebanon. Exposures to indoor mould can increase susceptibility to respiratory health risks in this vulnerable population, already impacted by multiple factors, from poor hygiene to displacement trauma and poverty. However, the low-cost renewable mitigation technologies developed here, offer a sustainable solution to remediate poor indoor air quality in refugee shelters accommodating displaced populations not only in Lebanon, but in refugee settings globally

Explanation–Question–Response dialogue: An argumentative tool for explainable AI

Advancements and deployments of AI-based systems, especially Deep Learning-driven generative language models, have accomplished impressive results over the past few years. Nevertheless, these remarkable achievements are intertwined with a related fear that such technologies might lead to a general relinquishing of our lives’s control to AIs. This concern, which also motivates the increasing interest in the eXplainable Artificial Intelligence (XAI) research field, is mostly caused by the opacity of the output of deep learning systems and the way that it is generated, which is largely obscure to laypeople. A dialectical interaction with such systems may enhance the users’ understanding and build a more robust trust towards AI. Commonly employed as specific formalisms for modelling intra-agent communications, dialogue games prove to be useful tools to rely upon when dealing with user’s explanation needs. The literature already offers some dialectical protocols that expressly handle explanations and their delivery. This paper fully formalises the novel Explanation–Question–Response (EQR) dialogue and its properties, whose main purpose is to provide satisfactory information (i.e., justified according to argumentative semantics) whilst ensuring a simplified protocol, in comparison with other existing approaches, for humans and artificial agents.This research was partially funded by the UK Engineering & Physical Sciences Research Council (EPSRC) under grant #EP/P010105/1

Brexit-like rhetoric on immigration no longer works

BlogAll articles posted on this blog give the views of the author(s), and not the position of LSE British Politics and Policy, nor of the London School of Economics and Political Science. Image credit: 1000 words on ShutterstockThe Government’s rhetoric on curbing immigration, and “stopping the small boats” in particular, has strong echoes of the Brexit campaign. As Matilde Rosina and Cristina Juverdeanu point out, the same themes, keywords, and even graphics are being used by the Government that were originally used by Brexit campaigners. The only difference is, this time the campaign doesn’t seem to be working

Improved Adversarial Transfer Network for Bearing Fault Diagnosis under Variable Working Conditions

Data Availability Statement: Data are contained within the article.Bearings are one of the critical components of rotating machinery, and their failure can cause catastrophic consequences. In this regard, previous studies have proposed a variety of intelligent diagnosis methods. Most existing bearing fault diagnosis methods implicitly assume that the training and test sets are from the same distribution. However, in real scenarios, bearings have been working in complex and changeable working environments for a long time. The data during their working processes and the data used for model training cannot meet this condition. This paper proposes an improved adversarial transfer network for fault diagnosis under variable working conditions. Specifically, this paper combines an adversarial transfer network with a short-time Fourier transform to obtain satisfactory results with the lighter network. Then, this paper employs a channel attention module to enhance feature fusion. Moreover, this paper designs a novel domain discrepancy hybrid metric loss to improve model transfer learning performance. Finally, this paper verifies the method’s effectiveness on three datasets, including dual-rotor, a Case Western Reserve University dataset and the Ottawa dataset. The proposed method achieves average accuracy, surpassing other methods, and shows better domain alignment capabilities.This work was supported in part by the Natural Science Foundation of China (No. 52175116), Major Research Programs of the Natural Science Foundation of China (No. 92060302), the Research Foundation of the Higher Educational Key Laboratory for Flexible Manufacturing Equipment Integration of Fujian Province, the Xiamen Institute of Technology, the National Key Science and Technology Infrastructure Opening Project Fund for Research and Evaluation facilities for Service Safety of Major Engineering Materials and the Aeronautical Science Foundation (No. 2019ZB070001). Also, this work was supported in part by the Royal Society award (number IEC\NSFC\223294) to Asoke K. Nandi. Jun Wang acknowledges the financial support from the Innovative Leading Talents Scholarship and Brunel University London

New laminar flame speed correlation for lean mixtures of hydrogen combustion with water addition under high pressure conditions

Hydrogen may become a substitute for liquid fossil fuels, contributing to greenhouse gas emissions reductions in internal combustion engines. Numerical simulations play a critical role in the advancement of these engines, with laminar flame speed being the main input. Experimental data of hydrogen flame speed at elevated pressures are scarce, due to the instability of the flames. Nonetheless, stable hydrogen flames can be predicted using chemical kinetics models. Moreover, the injection of water into the hydrogen fuelled engine could offer several benefits to engine combustion and emission performance, as it modulates the laminar flame speed within the combustion chamber and this effect has not been completely understood. Currently, no correlation exists to predict the laminar flame speed of hydrogen-air combustion with water addition under lean mixture engine operating conditions. In this study, we have extended the newly developed laminar flame speed correlation of hydrogen-air combustion to account for the effects of water addition under engine relevant conditions by using chemical kinetic laminar flame speed values. The laminar flame speed correlation was derived for pressures from 10 to 70 bar, temperatures from 400 to 800 K, equivalence ratios from 0.35 to 1 and water addition by mole from 0 to 20%. The hydrogen laminar flame speed correlation was expressed using polynomial forms with reduced order and number of terms with optimized values of coefficients. Additionally, a new exponential term was proposed to the power term α of the laminar flame speed correlation to capture the coupled effects of pressure and temperature on laminar flame speeds under engine-relevant lean burn water-diluted operating conditions

Changes in social norms during the early stages of the COVID-19 pandemic across 43 countries

Data availability: The data generated in this study have been deposited in the Open Science Framework (https://doi.org/10.17605/OSF.IO/STKFR). Non-experimental data included in our datasets (i.e., intensity of government response to COVID-19 is the Stringency Index, COVID-19 deaths and cases per million) are taken from the Oxford COVID−19 Government Response Tracker [22 Hale, T. et al. A global panel database of pandemic policies (Oxford COVID−19 Government Response Tracker). Nat. Human Behav. https://doi.org/10.1038/s41562-021-01079-8 (2021).] and Our World in Data [38 Ritchie, H. et al. Coronavirus Pandemic (COVID-19). Our World in Data. https://ourworldindata.org/coronavirus (2020).] (downloaded November 2020). Wave 0 data are from [3 Gelfand, M. J. et al. Differences between tight and loose cultures: a 33-nation study. Science 332, 1100–1104 (2011).[ and Wave 1 data are from [5 Eriksson, K. et al. Perceptions of the appropriate response to norm violation in 57 societies. Nat. Commun. 12, 1481 (2021).].Code availability: The survey and analysis code are available at the Open Science Framework (https://doi.org/10.17605/OSF.IO/STKFR).Supplementary information is available online at: https://www.nature.com/articles/s41467-024-44999-5#Sec40 .The emergence of COVID-19 dramatically changed social behavior across societies and contexts. Here we study whether social norms also changed. Specifically, we study this question for cultural tightness (the degree to which societies generally have strong norms), specific social norms (e.g. stealing, hand washing), and norms about enforcement, using survey data from 30,431 respondents in 43 countries recorded before and in the early stages following the emergence of COVID-19. Using variation in disease intensity, we shed light on the mechanisms predicting changes in social norm measures. We find evidence that, after the emergence of the COVID-19 pandemic, hand washing norms increased while tightness and punishing frequency slightly decreased but observe no evidence for a robust change in most other norms. Thus, at least in the short term, our findings suggest that cultures are largely stable to pandemic threats except in those norms, hand washing in this case, that are perceived to be directly relevant to dealing with the collective threat.Knut and Wallenberg Grant “How do human norms form and change?” 2016.0167. (G.An.). The Swedish Research Council grant “Norms & Risk: Do social norms help dealing with collective threats” 2021-06271 (G.An.). Ministero dell’Istruzione dell’Università e della Ricerca, PRIN 2017, prot. 20178TRM3F (D.B.). Universidad de Los Andes, Fondo Vicerrectoría de Investigaciones (J.-C.C.). Ministry of Innovation and Technology of Hungary, National Research, Development and Innovation Fund NKFIH-OTKA K135963 (M.F.). Grant 23-061770 S of the Czech Science Foundation (M.H. and S.G.). RVO: 68081740 of the Institute of Psychology, Czech Academy of Sciences (M.H. and S.G.). RA Science Committee, research project N.20TTSH-070 (A.Gr. and N.Khac.). Open University of Israel, 511687 (R.N.). HSE University Basic Research Program (E.O.). Project BASIC (PID2022-141802NB-I00) funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe” (A.Sá.). US Army Research Office Grant W911NF-19-1-910281 (B.S.). Netherlands Organisation for Scientific Research, 019.183SG.001 (E.S.). Netherlands Organisation for Scientific Research, VI.Veni.201 G.013 (E.S.). European Commission, Horizon 2020-ID 870827 (E.S.). UKRI Grant “Secret Power” No. EP/X02170X/1 awarded under the European Commission’s “European Research Council - STG” Scheme (G.A.T.)